SIMA: Python software for analysis of dynamic fluorescence imaging data - Kaifosh, Patrick; Zaremba, Jeffrey D.; Danielson, Nathan B.; Losonczy, Attila (Frontiers in Neuroinformatics, 2014)
Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic ﬂuorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to ﬂuorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged ﬁelds into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with ﬂexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/.